• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid

References

1 
Korea Rural Economic Institute, Agricultural Outlook 2021Google Search
2 
Korean Statistical Information Service, Changes in the structure of the livestock industry through statistics.Google Search
3 
National Institute of Animal Science, Enhancement of economic feasibility through proper management of bree- ding cattle using standard Korean cattle.Google Search
4 
Rural Development Administration, A tape measure that calculates the weight of Korean cattle without a scale.Google Search
5 
Rural Development Administration, Korean cattle standard weight calculator.Google Search
6 
K. Wang, H. Guo, Q. Ma, W. Su, L. Chen, D. Zhu, 2018, A portable and automatic Xtion-based measurement system for pig body size, Computers and Electronics in Agriculture 148, pp. 291-298DOI
7 
K. W. Seo, D. W. Lee, E. G. Choi, C. H. Kim, H. T. Kim, 2012, Algorithm for Measurement of the Dairy Cow’s Body Parameters by Using Image Processing, Journal of Biosystems Engineering 37.2, pp. 122-129DOI
8 
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, 1998, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86.11, pp. 2278-2324DOI
9 
B. Jiang, Q. Wu, X. Yin, D. Wu, H. Song, D. He, 2019, FLYOLOv3 deep learning for key parts of dairy cow body detection, Computers and Electronics in Agriculture 166 104982DOI
10 
D. Wu, Q. Wu, X. Yin, B. Jiang, H. Wang, D. He, H. Song, 2020, Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector, Biosystems Engineering 189, pp. 150-163DOI
11 
Apirachai Wongsriworaphon, Banchar Arnonkijpanich, Supachai Pathumnakul, 2015, An approach based on digital image analysis to estimate the live weights of pigs in farm environments, Computers and Electronics in Agriculture 115, pp. 26-33DOI
12 
Carsten Rother, Vladimir Kolmogorov, Andrew Blake, 2004, GrabCut interactive foreground extraction using iterated graph cuts, ACM transactions on graphics (TOG) 23.3, pp. 309-314DOI
13 
Georgios Banos, M. P. Coffey, 2012, Prediction of live- weight from linear conformation traits in dairy cattle, Journal of dairy science 95.4, pp. 2170-2175DOI
14 
X. Song, E. A. M. Bokkers, P. P. J. van der Tol, P. G. Koer- kamp, S. Van Mourik, 2018, Automated body weight prediction of dairy cows using 3-dimensional vision, Journal of dairy science, Vol. 101, No. 5, pp. 4448-4459DOI
15 
K. He, G. Gkioxari, P. Dollár, R. Girshick, 2017, Mask r-cnn, Proceedings of the IEEE international conference on computer vision, pp. 2961-2969Google Search
16 
S. Ren, K. He, R. Girshick, J. Sun, 2016, Faster R-CNN: towards real-time object detection with region proposal networks, IEEE transactions on pattern analysis and machine intelligence 39.6, pp. 1137-1149DOI
17 
Jonathan Long, Evan Shelhamer, 2015, Fully convolutional networks for semantic segmentation, Procee- dings of the IEEE conference on computer vision and pattern recognition, pp. 3431-3440Google Search
18 
G. Banos, M. P. Coffey, 2012, Prediction of liveweight from linear conformation traits in dairy cattle, Journal of dairy science, Vol. 95, No. 4, pp. 2170-2175DOI
19 
A. F. Fernandes, E. M. Turra, É. R. de Alvarenga, T. L. Passafaro, F. B. Lopes, G. F. Alves, G. J. Rosa, “Deep Learning image segmentation for extraction of fish body measurements and prediction of body weight and carcass traits in Nile tilapia, Computers and electronics in agriculture, 170, 105274Google Search
20 
A. H. Zhain, M. D. Guitara, B. U. Hidayahtuloh, R. A. Wibowo, H. F. Muttaqin, 2021, Digital Image Processing to Determine Weight and Classification of Cow Weight with Deep Learning, Psychology and Education Journal 58.1, pp. 6066-6082Google Search